http://hdl.handle.net/10016/6740 2013-06-18T05:38:21Z http://hdl.handle.net/10016/16319 Title: Photonic heterodyne pixel for imaging arrays at microwave and MM-Wave frequencies Author(s): Criado, Ángel Rubén; Montero-dePaz, Javier; Dios, Cristina de; García-Muñoz, Luis Enrique; Segovia, Daniel; Acedo, Pablo Abstract: The use of photonic heterodyne receivers based on semiconductor optical amplifiers to be used in imaging arrays at several GHz frequencies is evaluated. With this objective, a 3×3 imaging array based on such photonic pixels has been fabricated and characterized. Each of the receiving optoelectronic pixels is composed of an antipodal linear tapered slot antenna (LTSA) that sends the received RF signal directly to the electrical port of a semiconductor opticalamplifier (SOA) acting as the optoelectronic mixer. Both the local oscillator (LO) and the intermediate frequency (IF) signals are directly distributed to/from the array pixels using fiber optics, that allows for remote LO generation and IF processing to recover the image. The results shown in this work demonstrate that the performances of the optoelectronic imaging array are similar to a reference all-electronic array, revealing the possibility of using this photonic architecture in future high-density, scalable, compact imaging arrays in microwave and millimeter wave ranges. 2012-09-12T22:00:00Z http://hdl.handle.net/10016/11696 Title: Atlas-based automated positioning of outer volume suppression slices in short-TE 3D MR spectroscopic imaging of the human brain Author(s): Yung, Kaung-Ti; Zheng, Weili; Zhao, Chenguang; Van der Kouwe, André; Martínez-Ramón, Manel; Posse, Stefan Abstract: Spatial suppression of peripheral lipid-containing regions in volumetric MR spectroscopic imaging (MRSI) of the human brain requires placing large numbers of outer volume suppression (OVS) slices, which is time consuming, prone to operator error and may introduce subject-dependent variability in volume coverage. We developed a novel, computationally efficient atlas-based approach for automated positioning of up to 16 OVS slices and the MRSI slab. Standardized positions in MNI atlas space were established offline using a recently developed iterative optimization procedure. During the scanning session, positions in subject space were computed using affine transformation of standardized positions in MNI space. This atlas-based approach was characterized offline using MPRAGE data collected in 11 subjects. The method was further validated in 14 subjects on a clinical 3T scanner using 3D short TE (15-20ms) Proton-Echo-Planar-Spectroscopic-Imaging (PEPSI) in upper cerebrum. Comparison of manual and automatic placement using 8 OVS slices demonstrated consistent MRSI volume selection and comparable spectral quality with similar degree of lipid suppression and number of usable voxels. Automated positioning of 16 OVS slices enabled larger volume coverage, while maintaining similar spectral quality and lipid suppression. Atlas-based automatic prescription of short TE MRSI is expected to be advantageous for longitudinal and cross sectional studies 2011-04-04T22:00:00Z http://hdl.handle.net/10016/11691 Title: Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia Author(s): Castro, Eduardo; Martínez-Ramón, Manel; Pearlson, Godfrey; Sui, Jing; Calhoun, Vince D. Abstract: Pattern classification of brain imaging data can enable the automatic detection of differences in cognitive processes of specific groups of interest. Furthermore, it can also give neuroanatomical information related to the regions of the brain that are most relevant to detect these differences by means of feature selection procedures, which are also well-suited to deal with the high dimensionality of brain imaging data. This work proposes the application of recursive feature elimination using a machine learning algorithm based on composite kernels to the classification of healthy controls and patients with schizophrenia. This framework, which evaluates nonlinear relationships between voxels, analyzes whole-brain fMRI data from an auditory task experiment that is segmented into anatomical regions and recursively eliminates the uninformative ones based on their relevance estimates, thus yielding the set of most discriminative brain areas for group classification. The collected data was processed using two analysis methods: the general linear model (GLM) and independent component analysis (ICA). GLM spatial maps as well as ICA temporal lobe and default mode component maps were then input to the classifier. A mean classification accuracy of up to 95% estimated with a leave-two-out cross-validation procedure was achieved by doing multi-source data classification. In addition, it is shown that the classification accuracy rate obtained by using multi-source data surpasses that reached by using single-source data, hence showing that this algorithm takes advantage of the complimentary nature of GLM and ICA 2010-12-31T23:00:00Z http://hdl.handle.net/10016/11690 Title: Learning non-linear time scales with Kernel γ-Filters Author(s): Camps-Valls, Gustavo; Muñoz-Marí, Jordi; Martínez-Ramón, Manel; Requena-Carrión, Jesús; Rojo-Álvarez, José Luis Abstract: A family of kernel methods, based on the γ-filter structure, is presented for non-linear system identification and time series prediction. The kernel trick allows us to develop the natural non-linear extension of the (linear) support vector machine (SVM) γ-filter, but this approach yields a rigid system model without non-linear cross relation between time-scales. Several functional analysis properties allow us to develop a full, principled family of kernel γ-filters. The improved performance in several application examples suggests that a more appropriate representation of signal states is achieved. 2008-12-31T23:00:00Z